Research on safety evaluation model for in-vehicle secondary task driving. (August 2015)

• Record Type: Journal Article
• Authors: Jin, Lisheng; Xian, Huacai; Niu, Qingning; Bie, Jing
• Is Part Of: Accident analysis and prevention. Volume 81(2015)
• Page Start: 243
• Page End: 250
• Publication Date: 2015-08
• Subjects: Driving safety -- Multi-task driving -- Driver eye movements -- ANP -- FCE; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2014.08.013
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: Measures of fixations, saccades, and blinks are collected and analyzed. A driving safety evaluation index system of driver eye movements is proposed. A driving safety evaluation model is built by using method of ANP theory and FCE method. Driving with secondary tasks has significant effects on driving safety. Abstract: This paper presents a new method for evaluating in-vehicle secondary task driving safety. There are five in-vehicle distracter tasks: tuning the radio to a local station, touching the touch-screen telephone menu to a certain song, talking with laboratory assistant, answering a telephone via Bluetooth headset, and finding the navigation system from Ipad4 computer. Forty young drivers completed the driving experiment on a driving simulator. Measures of fixations, saccades, and blinks are collected and analyzed. Based on the measures of driver eye movements which have significant difference between the baseline and secondary task driving conditions, the evaluation index system is built. The Analytic Network Process (ANP) theory is applied for determining the importance weight of the evaluation index in a fuzzy environment. On the basis of the importance weight of the evaluation index, Fuzzy Comprehensive Evaluation (FCE) method is utilized to evaluate the secondary task driving safety. Results show that driving with secondary tasks greatly distracts the driver's attention from road and the evaluation model built in this study could estimate driving safety effectively under different driving conditions.